Data Mining, Southeast Asia EditionElsevier, 2006 M04 6 - 800 pages Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge. Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data— including stream data, sequence data, graph structured data, social network data, and multi-relational data.
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... sections are introduced: Section 1.7 is on data mining primitives, which allow users to interactively communicate with data mining systems in order to direct the mining process, and Section 1.8 discusses the issues regarding how to ...
... sections, Xifeng Yan to the graph mining section, and Xiaoxin Yin to the multirelational data mining section. Hong Cheng, Charios Ermopoulos, Hector Gonzalez, David J. Hill, Chulyun Kim, Sangkyum Kim, Chao Liu, Hongyan Liu, Kasif ...
... Section 1.3.1), data modeling tools, and indexing and accessing methods. In addition, users gained convenient and flexible data access through query languages, user interfaces, optimized query processing, and transaction management ...
... (Section 2.3), data integration and transformation (Section 2.4), and data reduction (Section 2.5). Concept hierarchies can be used in an alternative form of data reduction where we replace low-level data (such as raw values for age) ...
... Section 2.4. “Hmmm,” you wonder, as you consider your data even further. “The data set I have selected for analysis is HUGE, which is sure to slow down the mining process. Is there any way I can reduce the size of my data set, without ...
Contents
1 | |
47 | |
105 | |
4 Data Cube Computation and Data Generalization | 157 |
5 Mining Frequent Patterns Associations and Correlations | 227 |
6 Classification and Prediction | 285 |
7 Cluster Analysis | 383 |
8 Mining Stream TimeSeries and Sequence Data | 467 |
9 Graph Mining Social Network Analysis and Multirelational Data Mining | 535 |
10 Mining Object Spatial Multimedia Text and Web Data | 591 |
11 Applications and Trends in Data Mining | 649 |
An Introduction to Microsofts OLE DB for Data Mining | 691 |
Bibliography | 703 |
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Geographic Data Mining and Knowledge Discovery Harvey J. Miller,Jiawei Han No preview available - 2003 |